2,364 research outputs found
Non-convex regularization in remote sensing
In this paper, we study the effect of different regularizers and their
implications in high dimensional image classification and sparse linear
unmixing. Although kernelization or sparse methods are globally accepted
solutions for processing data in high dimensions, we present here a study on
the impact of the form of regularization used and its parametrization. We
consider regularization via traditional squared (2) and sparsity-promoting (1)
norms, as well as more unconventional nonconvex regularizers (p and Log Sum
Penalty). We compare their properties and advantages on several classification
and linear unmixing tasks and provide advices on the choice of the best
regularizer for the problem at hand. Finally, we also provide a fully
functional toolbox for the community.Comment: 11 pages, 11 figure
Bias in data-driven artificial intelligence systemsâAn introductory survey
Artificial Intelligence (AI)-based systems are widely employed nowadays to make decisions that have far-reaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well-grounded in a legal frame. In this survey, we focus on data-driven AI, as a large part of AI is powered nowadays by (big) data and powerful machine learning algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features such as race, sex, and so forth. This article is categorized under: Commercial, Legal, and Ethical Issues > Fairness in Data Mining Commercial, Legal, and Ethical Issues > Ethical Considerations Commercial, Legal, and Ethical Issues > Legal Issues
Bias in data-driven artificial intelligence systems - An introductory survey
Artificial Intelligence (AI)âbased systems are widely employed nowadays to make decisions that have farâreaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches wellâgrounded in a legal frame. In this survey, we focus on dataâdriven AI, as a large part of AI is powered nowadays by (big) data and powerful machine learning algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features such as race, sex, and so forth
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Bias in data-driven artificial intelligence systems - An introductory survey
Artificial Intelligence (AI)-based systems are widely employed nowadays to make decisions that have far-reaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well-grounded in a legal frame. In this survey, we focus on data-driven AI, as a large part of AI is powered nowadays by (big) data and powerful machine learning algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features such as race, sex, and so forth. This article is categorized under: Commercial, Legal, and Ethical Issues > Fairness in Data Mining Commercial, Legal, and Ethical Issues > Ethical Considerations Commercial, Legal, and Ethical Issues > Legal Issues
Toward Generalizable Machine Learning Models in Speech, Language, and Hearing Sciences: Power Analysis and Sample Size Estimation
This study's first purpose is to provide quantitative evidence that would
incentivize researchers to instead use the more robust method of nested
cross-validation. The second purpose is to present methods and MATLAB codes for
doing power analysis for ML-based analysis during the design of a study. Monte
Carlo simulations were used to quantify the interactions between the employed
cross-validation method, the discriminative power of features, the
dimensionality of the feature space, and the dimensionality of the model. Four
different cross-validations (single holdout, 10-fold, train-validation-test,
and nested 10-fold) were compared based on the statistical power and
statistical confidence of the ML models. Distributions of the null and
alternative hypotheses were used to determine the minimum required sample size
for obtaining a statistically significant outcome ({\alpha}=0.05,
1-\b{eta}=0.8). Statistical confidence of the model was defined as the
probability of correct features being selected and hence being included in the
final model. Our analysis showed that the model generated based on the single
holdout method had very low statistical power and statistical confidence and
that it significantly overestimated the accuracy. Conversely, the nested
10-fold cross-validation resulted in the highest statistical confidence and the
highest statistical power, while providing an unbiased estimate of the
accuracy. The required sample size with a single holdout could be 50% higher
than what would be needed if nested cross-validation were used. Confidence in
the model based on nested cross-validation was as much as four times higher
than the confidence in the single holdout-based model. A computational model,
MATLAB codes, and lookup tables are provided to assist researchers with
estimating the sample size during the design of their future studies.Comment: Under review at JSLH
Better Optimism By Bayes: Adaptive Planning with Rich Models
The computational costs of inference and planning have confined Bayesian
model-based reinforcement learning to one of two dismal fates: powerful
Bayes-adaptive planning but only for simplistic models, or powerful, Bayesian
non-parametric models but using simple, myopic planning strategies such as
Thompson sampling. We ask whether it is feasible and truly beneficial to
combine rich probabilistic models with a closer approximation to fully Bayesian
planning. First, we use a collection of counterexamples to show formal problems
with the over-optimism inherent in Thompson sampling. Then we leverage
state-of-the-art techniques in efficient Bayes-adaptive planning and
non-parametric Bayesian methods to perform qualitatively better than both
existing conventional algorithms and Thompson sampling on two contextual
bandit-like problems.Comment: 11 pages, 11 figure
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